Evidence map›Paper›PMID 41193893›Full record

ArticleCNS neuroscience & therapeutics2025

Treatment-Specific Risk Scales for Identifying High-Risk Patients With Poor Prognosis in Acute Ischemic Stroke: A Cohort Study From the National Neurological Medical Center of China.

Yi Xu, Shenyi Kuang, Shilin Yang, Jianfeng Luo, Chun Yu, Xiaocui Kang, Xiang Han, Qiang Dong

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In one paragraph

Article in CNS neuroscience & therapeutics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Yi XuDepartment of Neurology, Huashan Hospital Fudan University, Shanghai, China.ORCID 0009-0009-5053-3925
Shenyi KuangDepartment of Neurology, Huashan Hospital Fudan University, Shanghai, China.
Shilin YangDepartment of Neurology, Huashan Hospital Fudan University, Shanghai, China.ORCID 0000-0001-8480-541X
Jianfeng LuoSchool of Public Health, Fudan University, Shanghai, China.
Chun YuIntensive Care Unit of West Campus, Huashan Hospital Fudan University, Shanghai, China.
Xiaocui KangDepartment of Neurology, Fifth People's Hospital of Shanghai Fudan University, Shanghai, China.
Xiang HanDepartment of Neurology, Huashan Hospital Fudan University, Shanghai, China.ORCID 0000-0003-4608-2083
Qiang DongDepartment of Neurology, Huashan Hospital Fudan University, Shanghai, China.ORCID 0000-0002-3874-0130

Funding

National Natural Science Foundation of China 82202799National Natural Science Foundation of China 82271350Programs from Shanghai Stroke Association SSA-2020-020-2State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science of Fudan University
6 · The paper itself

Abstract

aimsTo develop and validate a user-friendly scale for predicting acute-phase adverse outcomes in acute ischemic stroke (AIS), thereby optimizing clinical management.

methodsThis retrospective study enrolled AIS patients within 72 h of onset (excluding thrombectomy), stratified according to thrombolysis status to develop treatment-specific prognostic models. The prognostic scale of AIS acute stage based on treatment stratification (PAIST) was developed using clinical variables, with discharge mRS as the primary endpoint, followed by external validation.

resultsA total of 1971 AIS patients (437 thrombolyzed) were included. Both thrombolysis-specific and non-thrombolysis-specific models incorporated core predictors (baseline NIHSS, deep vein thrombosis, neuron specific enolase, neutrophil percentage) but differed in cut-off values and weightings. Additionally, the non-thrombolysis-specific model integrated three extra variables: age, fasting blood glucose, and serum potassium. External validation demonstrated PAIST outperformed the benchmark model (AUCs: thrombolysis group 0.759 vs. 0.698; non-thrombolysis group 0.850 vs. 0.801; all p ≤ 0.05). PAIST-based risk stratification effectively identified high-risk patients, with poor prognosis rates of 76.92% (thrombolysis group) and 61.11% (non-thrombolysis group).

conclusionThe PAIST scale is an effective and practical tool for acute-phase prognostic risk stratification in AIS. Its treatment-stratified design enables accurate risk assessment, thereby supporting individualized clinical decision-making.

Indexed as

Brain IschemiaIschemic StrokeThrombolytic TherapyAgedAged, 80 and overChinaCohort StudiesFemaleHumansMaleMiddle AgedPrognosisRetrospective StudiesRisk AssessmentRisk Factorsacute ischemic strokeacute‐phase prognostic modelnomogramrisk factors

Identifiers

PMID41193893
PMCPMC12588881

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